Stock Return Predictability : A Bayesian Model Selection Perspective

Attempts to characterize stock return predictability have generated a plethora of papers documenting the ability of various variables to explain conditional expected returns. However, there is little consensus on what the important conditioning variables are, giving rise to a great deal of model uncertainty and data snooping fears. In this paper, we introduce a new methodology that explicitly takes the model uncertainty into account by comparing all possible models simultaneously and in which the priors are calibrated to reflect economically meaningful prior information. Therefore, our approach minimizes data snooping given the information set and the priors. We compare the prior views of a skeptic and a confident investor. The data imply posterior probabilities that are in general more supportive of stock return predictability than the priors for both types of investors, over a wide range of prior views. Furthermore, the stalwarts such as dividends and past returns do not perform well. The out-of-sample results for the Bayesian average models show improved forecasts relative to the classical statistical model selection methods, are consistent with the in-sample results and show some, albeit small, evidence of predictability